WAW School on Complex Networks and Graph Models, Eindhoven University of Technology
December 7-9, 2015
Talk on joint work with David F. Gleich:
Graph diffusions like PageRank are increasingly used for fast, personalized graph computations. In particular, local graph diffusions have recently been designed for data mining applications like community detection, node centrality, link prediction, semi-supervised learning, graph kernels, and more. A growing literature in conferences like KDD, ICML, WWW, and WAW explores uses, properties, and efficient means of computing these diffusions. Moreover, data scientists at well- known companies are studying these tools for tasks like product categorization, targeted advertising, and clustering. Our course covers up to the state of the art in fast methods for local graph diffusions. This includes a discussion of random walks and graph structures to explain why diffusions are being used, and an introduction to local algorithms and sparse coordinate-relaxation-based linear solvers to derive convergence properties and implementation details. We’ll present application demos, and enough background to understand the algorithms and directions for future work.